Person Re-Identification by Pose Invariant Deep Metric Learning With Improved Triplet Loss

Person re-identification (re-ID) is a challenging problem in the community which aims at identifying person in a surveillance video. Despite recent advance in the field of computer vision, person re-ID still presents great challenge since person’s presence is various under different illumination, viewpoints, occlusion, and background clutter. In this paper, to exploit more discriminative information of person’s appearance, we propose a novel pose invariant deep metric learning (PIDML) method under an improved triplet loss for person re-ID. Our approach contributes the misalignment problem and distance metric simultaneously, which are two key problems for person re-ID. Extensive experiments show that our proposed method could achieve favorable accuracy while compared with the state-of-the-art techniques on the challenging Market-1501, CUHK03, and VIPeR datasets.